{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_boston\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import random"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])\n"
     ]
    },
    {
     "data": {
      "text/plain": "         0     1     2    3      4      5     6       7    8      9    10  \\\n0  0.00632  18.0  2.31  0.0  0.538  6.575  65.2  4.0900  1.0  296.0  15.3   \n1  0.02731   0.0  7.07  0.0  0.469  6.421  78.9  4.9671  2.0  242.0  17.8   \n2  0.02729   0.0  7.07  0.0  0.469  7.185  61.1  4.9671  2.0  242.0  17.8   \n3  0.03237   0.0  2.18  0.0  0.458  6.998  45.8  6.0622  3.0  222.0  18.7   \n4  0.06905   0.0  2.18  0.0  0.458  7.147  54.2  6.0622  3.0  222.0  18.7   \n\n       11    12  \n0  396.90  4.98  \n1  396.90  9.14  \n2  392.83  4.03  \n3  394.63  2.94  \n4  396.90  5.33  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>4</th>\n      <th>5</th>\n      <th>6</th>\n      <th>7</th>\n      <th>8</th>\n      <th>9</th>\n      <th>10</th>\n      <th>11</th>\n      <th>12</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0.0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1.0</td>\n      <td>296.0</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=load_boston()\n",
    "print(data.keys())\n",
    "dataframe = pd.DataFrame(data['data'])\n",
    "dataframe.head(5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "outputs": [
    {
     "data": {
      "text/plain": "array(['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD',\n       'TAX', 'PTRATIO', 'B', 'LSTAT'], dtype='<U7')"
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['feature_names']\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "outputs": [
    {
     "data": {
      "text/plain": "      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD    TAX  \\\n0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900  1.0  296.0   \n1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671  2.0  242.0   \n2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671  2.0  242.0   \n3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622  3.0  222.0   \n4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622  3.0  222.0   \n\n   PTRATIO       B  LSTAT  price  \n0     15.3  396.90   4.98   24.0  \n1     17.8  396.90   9.14   21.6  \n2     17.8  392.83   4.03   34.7  \n3     18.7  394.63   2.94   33.4  \n4     18.7  396.90   5.33   36.2  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>CRIM</th>\n      <th>ZN</th>\n      <th>INDUS</th>\n      <th>CHAS</th>\n      <th>NOX</th>\n      <th>RM</th>\n      <th>AGE</th>\n      <th>DIS</th>\n      <th>RAD</th>\n      <th>TAX</th>\n      <th>PTRATIO</th>\n      <th>B</th>\n      <th>LSTAT</th>\n      <th>price</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.00632</td>\n      <td>18.0</td>\n      <td>2.31</td>\n      <td>0.0</td>\n      <td>0.538</td>\n      <td>6.575</td>\n      <td>65.2</td>\n      <td>4.0900</td>\n      <td>1.0</td>\n      <td>296.0</td>\n      <td>15.3</td>\n      <td>396.90</td>\n      <td>4.98</td>\n      <td>24.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.02731</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>6.421</td>\n      <td>78.9</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>396.90</td>\n      <td>9.14</td>\n      <td>21.6</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.02729</td>\n      <td>0.0</td>\n      <td>7.07</td>\n      <td>0.0</td>\n      <td>0.469</td>\n      <td>7.185</td>\n      <td>61.1</td>\n      <td>4.9671</td>\n      <td>2.0</td>\n      <td>242.0</td>\n      <td>17.8</td>\n      <td>392.83</td>\n      <td>4.03</td>\n      <td>34.7</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.03237</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>6.998</td>\n      <td>45.8</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>394.63</td>\n      <td>2.94</td>\n      <td>33.4</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.06905</td>\n      <td>0.0</td>\n      <td>2.18</td>\n      <td>0.0</td>\n      <td>0.458</td>\n      <td>7.147</td>\n      <td>54.2</td>\n      <td>6.0622</td>\n      <td>3.0</td>\n      <td>222.0</td>\n      <td>18.7</td>\n      <td>396.90</td>\n      <td>5.33</td>\n      <td>36.2</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.columns = data['feature_names']\n",
    "dataframe['price']=data['target']\n",
    "dataframe.head(5)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.axes._subplots.AxesSubplot at 0x210732ef040>"
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 2 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(dataframe.corr(), annot=True ,fmt='.1g')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "LSTAT     -0.737663\nPTRATIO   -0.507787\nINDUS     -0.483725\nTAX       -0.468536\nNOX       -0.427321\nCRIM      -0.388305\nRAD       -0.381626\nAGE       -0.376955\nCHAS       0.175260\nDIS        0.249929\nB          0.333461\nZN         0.360445\nRM         0.695360\nprice      1.000000\nName: price, dtype: float64"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe.corr()['price'].sort_values()"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x210736f9490>"
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dataframe['RM'], dataframe['price'])\n",
    "# print(dataframe['price'])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## knn"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_items([(6.575, 24.0), (6.421, 21.6), (7.185, 34.9), (6.998, 33.4), (7.147, 36.2), (6.43, 28.7), (6.012, 22.9), (6.172, 27.1), (5.631, 16.5), (6.004, 20.3), (6.377, 15.0), (6.009, 21.7), (5.889, 21.7), (5.949, 20.4), (6.096, 13.5), (5.834, 19.9), (5.935, 8.4), (5.99, 17.5), (5.456, 20.2), (5.727, 18.2), (5.57, 13.6), (5.965, 19.6), (6.142, 15.2), (5.813, 16.6), (5.924, 15.6), (5.599, 13.9), (6.047, 14.8), (6.495, 26.4), (6.674, 21.0), (5.713, 20.1), (6.072, 14.5), (5.95, 13.2), (5.701, 13.1), (5.933, 18.9), (5.841, 20.0), (5.85, 21.0), (5.966, 16.0), (6.595, 30.8), (7.024, 34.9), (6.77, 26.6), (6.169, 25.3), (6.211, 25.0), (6.069, 21.2), (5.682, 19.3), (5.786, 20.0), (6.03, 11.9), (5.399, 14.4), (5.602, 19.4), (5.963, 19.7), (6.115, 20.5), (6.511, 25.0), (5.998, 23.4), (5.888, 23.3), (7.249, 35.4), (6.383, 24.7), (6.816, 31.6), (6.145, 23.3), (5.927, 19.6), (5.741, 18.7), (6.456, 22.2), (6.762, 25.0), (7.104, 33.0), (6.29, 23.5), (5.787, 19.4), (5.878, 22.0), (5.594, 17.4), 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(5.468, 15.6), (4.903, 11.8), (6.13, 13.8), (5.628, 15.6), (4.926, 14.6), (5.186, 17.8), (5.597, 15.4), (6.122, 22.1), (5.404, 19.3), (5.012, 15.3), (5.709, 19.4), (6.129, 17.0), (6.152, 8.7), (5.272, 13.1), (6.943, 41.3), (6.066, 24.3), (6.51, 23.3), (6.25, 27.0), (7.489, 50.0), (7.802, 50.0), (8.375, 50.0), (5.854, 10.8), (6.101, 25.0), (7.929, 50.0), (5.877, 23.8), (6.319, 23.8), (6.402, 22.3), (5.875, 50.0), (5.88, 19.1), (5.572, 23.1), (6.416, 23.6), (5.859, 22.6), (6.546, 29.4), (6.02, 23.2), (6.315, 22.3), (6.86, 29.9), (6.98, 29.8), (7.765, 39.8), (6.144, 19.8), (7.155, 37.9), (6.563, 32.5), (5.604, 26.4), (6.153, 29.6), (7.831, 50.0), (6.782, 7.5), (6.556, 29.8), (6.951, 26.7), (6.739, 30.5), (7.178, 36.4), (6.8, 31.1), (6.604, 29.1), (7.875, 50.0), (7.287, 33.3), (7.107, 30.3), (7.274, 34.6), (6.975, 34.9), (7.135, 32.9), (6.162, 13.3), (7.61, 42.3), (7.853, 48.5), (8.034, 50.0), (5.891, 22.6), (5.783, 22.5), (6.064, 24.4), (5.344, 20.0), (5.96, 21.7), (5.807, 22.4), (6.375, 28.1), (5.412, 23.7), (6.182, 25.0), (6.642, 28.7), (5.951, 21.5), (6.373, 23.0), (6.164, 21.7), (6.879, 27.5), (6.618, 30.1), (8.266, 44.8), (8.725, 50.0), (8.04, 37.6), (7.163, 31.6), (7.686, 46.7), (6.552, 31.5), (5.981, 24.3), (7.412, 31.7), (8.337, 41.7), (8.247, 48.3), (6.726, 29.0), (6.086, 24.0), (6.631, 25.1), (7.358, 31.5), (6.481, 23.7), (6.606, 23.3), (6.897, 22.0), (6.095, 20.1), (6.358, 22.2), (6.393, 23.7), (5.593, 17.6), (5.605, 18.5), (6.108, 21.9), (6.226, 20.5), (6.433, 24.5), (6.718, 26.2), (6.487, 24.4), (6.438, 24.8), (6.957, 29.6), (8.259, 42.8), (5.876, 20.9), (7.454, 44.0), (8.704, 50.0), (7.333, 36.0), (6.842, 30.1), (7.203, 33.8), (7.52, 43.1), (8.398, 48.8), (7.327, 31.0), (7.206, 36.5), (5.56, 22.8), (7.014, 30.7), (8.297, 50.0), (7.47, 43.5), (5.92, 20.7), (6.24, 25.2), (6.538, 24.4), (7.691, 35.2), (6.758, 32.4), (6.854, 32.0), (7.267, 33.2), (6.826, 33.1), (6.482, 29.1), (6.812, 35.1), (6.968, 10.4), (7.645, 46.0), (7.923, 50.0), (7.088, 32.2), (6.453, 22.0), (6.23, 20.1), (6.209, 21.4), (6.565, 24.8), (6.861, 28.5), (7.148, 37.3), (6.678, 28.6), (6.549, 27.1), (5.79, 20.3), (6.345, 22.5), (7.041, 29.0), (6.871, 24.8), (6.59, 22.0), (6.982, 33.1), (7.236, 36.1), (6.616, 28.4), (7.42, 33.4), (6.849, 28.2), (6.635, 24.5), (5.972, 20.3), (4.973, 16.1), (6.023, 19.4), (6.266, 21.6), (6.567, 23.8), (5.705, 16.2), (5.914, 17.8), (5.782, 19.8), (6.382, 23.1), (6.113, 21.0), (6.426, 23.8), (6.376, 17.7), (6.041, 20.4), (5.708, 18.5), (6.415, 25.0), (6.312, 21.2), (6.083, 22.2), (5.868, 19.3), (6.333, 22.6), (5.706, 17.1), (6.031, 19.4), (6.316, 22.2), (6.31, 20.7), (6.037, 21.1), (5.869, 19.5), (5.895, 18.5), (6.059, 20.6), (5.985, 19.0), (5.968, 18.7), (7.241, 32.7), (6.54, 16.5), (6.696, 23.9), (6.874, 31.2), (6.014, 17.5), (5.898, 17.2), (6.516, 23.1), (6.939, 26.6), (6.49, 22.9), (6.579, 24.1), (5.884, 18.6), (6.728, 14.9), (5.663, 18.2), (5.936, 13.5), (6.212, 17.8), (6.395, 21.7), (6.112, 22.6), (6.398, 25.0), (6.251, 12.6), (5.362, 20.8), (5.803, 16.8), (8.78, 21.9), (3.561, 27.5), (4.963, 21.9), (3.863, 23.1), (4.97, 50.0), (6.683, 50.0), (7.016, 50.0), (6.216, 50.0), (4.906, 13.8), (4.138, 11.9), (7.313, 15.0), (6.649, 13.9), (6.794, 22.0), (6.38, 9.5), (6.223, 10.2), (6.545, 10.9), (5.536, 11.3), (5.52, 12.3), (4.368, 8.8), (5.277, 7.2), (4.652, 10.5), (5.0, 7.4), (4.88, 10.2), (5.39, 19.7), (6.051, 23.2), (5.036, 9.7), (6.193, 11.0), (5.887, 12.7), (6.471, 13.1), (5.747, 8.5), (5.453, 5.0), (5.852, 6.3), (5.987, 5.6), (6.343, 7.2), (6.404, 12.1), (5.349, 8.3), (5.531, 8.5), (5.683, 5.0), (5.608, 27.9), (5.617, 17.2), (6.852, 27.5), (6.657, 17.2), (4.628, 17.9), (5.155, 16.3), (4.519, 7.0), (6.434, 7.2), (5.304, 12.0), (5.957, 8.8), (6.824, 8.4), (6.411, 16.7), (6.006, 14.2), (5.648, 20.8), (6.103, 13.4), (5.565, 11.7), (5.896, 8.3), (5.837, 10.2), (6.202, 10.9), (6.348, 14.5), (6.833, 14.1), (6.425, 16.1), (6.436, 14.3), (6.208, 11.7), (6.629, 13.4), (6.461, 9.6), (5.627, 12.8), (5.818, 10.5), (6.406, 17.1), (6.219, 18.4), (6.485, 15.4), (6.459, 11.8), (6.341, 14.9), (6.185, 14.6), (6.749, 13.4), (6.655, 15.2), (6.297, 16.1), (7.393, 17.8), (6.525, 14.1), (5.976, 12.7), (6.301, 14.9), (6.081, 20.0), (6.701, 16.4), (6.317, 19.5), (6.513, 20.2), (5.759, 19.9), (5.952, 19.0), (6.003, 19.1), (5.926, 24.5), (6.437, 23.2), (5.427, 13.8), (6.484, 16.7), (6.242, 23.0), (6.75, 23.7), (7.061, 25.0), (5.762, 21.8), (5.871, 20.6), (6.114, 19.1), (5.905, 20.6), (5.454, 15.2), (5.414, 7.0), (5.093, 8.1), (5.983, 20.1), (5.707, 21.8), (5.67, 23.1), (5.794, 18.3), (6.019, 21.2), (5.569, 17.5), (6.027, 16.8), (6.593, 22.4), (6.12, 20.6), (6.976, 23.9)])\n"
     ]
    }
   ],
   "source": [
    "dic = {a:b for a,b in zip(dataframe['RM'], dataframe['price']) }\n",
    "print(dic.items())"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "def knn(rm, history, k=3):\n",
    "    sorted_dic = sorted(dic.items(), key=lambda a:(a[0]-rm)**2)\n",
    "    nsortendic= sorted_dic[:k]\n",
    "    price_list= [b for a,b in nsortendic]\n",
    "    return np.mean(price_list)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [
    {
     "data": {
      "text/plain": "15.700000000000001"
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "knn(5.4,dic)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "##  LOSS:randam"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [],
   "source": [
    "def loss(y_hat, y):\n",
    "    return np.mean((y_hat-y)**2)\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "at the 0 we found the best answer the k is -5               the b is -26 the loss is 6534.541051729252\n",
      "at the 11 we found the best answer the k is 6               the b is -52 the loss is 1404.422422316206\n",
      "at the 16 we found the best answer the k is 9               the b is -49 the loss is 267.73942926284553\n",
      "at the 19 we found the best answer the k is 16               the b is -79 the loss is 68.00120164426876\n",
      "at the 315 we found the best answer the k is 3               the b is 3 the loss is 62.407329294466344\n",
      "at the 991 we found the best answer the k is 10               the b is -38 the loss is 49.35022272727274\n",
      "at the 1381 we found the best answer the k is 10               the b is -39 the loss is 45.72314762845849\n",
      "at the 2147 we found the best answer the k is 8               the b is -27 the loss is 44.752937169960475\n",
      "at the 6100 we found the best answer the k is 8               the b is -28 the loss is 44.26439962055332\n"
     ]
    }
   ],
   "source": [
    "min_loss=float('inf')\n",
    "number=10000\n",
    "best_k, best_b= None,None\n",
    "for i in range(number):\n",
    "    k,b = random.randrange(-100,100), random.randrange(-100,100)\n",
    "    y_hat=[k*x+b for x in dataframe['RM']]\n",
    "    loss1=loss(y_hat, dataframe['price'])\n",
    "    if loss1 < min_loss:\n",
    "        min_loss=loss1\n",
    "        best_k=k\n",
    "        best_b=b\n",
    "        print('at the {} we found the best answer the k is {} \\\n",
    "              the b is {} the loss is {}'.format(i,best_k, best_b,min_loss ))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.collections.PathCollection at 0x21071def3d0>"
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(dataframe['RM'],dataframe['price'])\n",
    "plt.scatter(dataframe['RM'], best_k*dataframe['RM']+best_b)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "## 蒙特卡洛\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%% md\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "we found in 0 k0.7905777140711443,b0.7905777140711443 ,loss1216.4948111692227\n",
      "we found in 1 k-3.3692688474929895,b-3.3692688474929895 ,loss175.76105737105541\n",
      "we found in 2 k-4.183011335687619,b-4.183011335687619 ,loss135.80696932730137\n",
      "we found in 3 k-4.3400569230228925,b-4.3400569230228925 ,loss134.25957287357218\n",
      "we found in 4 k-4.368221561689562,b-4.368221561689562 ,loss134.1639757013294\n",
      "we found in 5 k-4.3710930170097795,b-4.3710930170097795 ,loss134.1189166481686\n",
      "we found in 6 k-4.369001150201535,b-4.369001150201535 ,loss134.0747652394415\n",
      "we found in 7 k-4.365935846002302,b-4.365935846002302 ,loss134.03046176016124\n",
      "we found in 8 k-4.362680148498762,b-4.362680148498762 ,loss133.9861325356613\n",
      "we found in 9 k-4.359387735696724,b-4.359387735696724 ,loss133.94181521823444\n",
      "we found in 10 k-4.356088767876364,b-4.356088767876364 ,loss133.89751768293283\n",
      "we found in 11 k-4.35278916405512,b-4.35278916405512 ,loss133.85324148545052\n",
      "we found in 12 k-4.349490085724235,b-4.349490085724235 ,loss133.80898692320494\n",
      "we found in 13 k-4.346191760675457,b-4.346191760675457 ,loss133.76475404602445\n",
      "we found in 14 k-4.342894233455177,b-4.342894233455177 ,loss133.7205428551356\n",
      "we found in 15 k-4.339597512646573,b-4.339597512646573 ,loss133.67635334223164\n",
      "we found in 16 k-4.336301599774816,b-4.336301599774816 ,loss133.63218549713957\n",
      "we found in 17 k-4.333006494979936,b-4.333006494979936 ,loss133.58803930932422\n",
      "we found in 18 k-4.329712198130151,b-4.329712198130151 ,loss133.54391476818398\n",
      "we found in 19 k-4.326418709040377,b-4.326418709040377 ,loss133.49981186310794\n",
      "we found in 20 k-4.323126027515106,b-4.323126027515106 ,loss133.4557305834877\n",
      "we found in 21 k-4.319834153356825,b-4.319834153356825 ,loss133.41167091871955\n",
      "we found in 22 k-4.316543086367667,b-4.316543086367667 ,loss133.36763285820476\n",
      "we found in 23 k-4.313252826349731,b-4.313252826349731 ,loss133.32361639135007\n",
      "we found in 24 k-4.309963373105152,b-4.309963373105152 ,loss133.27962150756719\n",
      "we found in 25 k-4.30667472643611,b-4.30667472643611 ,loss133.235648196273\n",
      "we found in 26 k-4.30338688614483,b-4.30338688614483 ,loss133.19169644688958\n",
      "we found in 27 k-4.300099852033592,b-4.300099852033592 ,loss133.14776624884436\n",
      "we found in 28 k-4.2968136239047166,b-4.2968136239047166 ,loss133.10385759156986\n",
      "we found in 29 k-4.293528201560577,b-4.293528201560577 ,loss133.05997046450366\n",
      "we found in 30 k-4.290243584803596,b-4.290243584803596 ,loss133.01610485708866\n",
      "we found in 31 k-4.286959773436242,b-4.286959773436242 ,loss132.97226075877302\n",
      "we found in 32 k-4.283676767261034,b-4.283676767261034 ,loss132.9284381590097\n",
      "we found in 33 k-4.280394566080538,b-4.280394566080538 ,loss132.8846370472571\n",
      "we found in 34 k-4.277113169697369,b-4.277113169697369 ,loss132.84085741297895\n",
      "we found in 35 k-4.273832577914191,b-4.273832577914191 ,loss132.79709924564395\n",
      "we found in 36 k-4.270552790533714,b-4.270552790533714 ,loss132.75336253472582\n",
      "we found in 37 k-4.2672738073587,b-4.2672738073587 ,loss132.7096472697039\n",
      "we found in 38 k-4.263995628191956,b-4.263995628191956 ,loss132.665953440062\n",
      "we found in 39 k-4.260718252836338,b-4.260718252836338 ,loss132.62228103528977\n",
      "we found in 40 k-4.257441681094754,b-4.257441681094754 ,loss132.57863004488144\n",
      "we found in 41 k-4.254165912770155,b-4.254165912770155 ,loss132.53500045833704\n",
      "we found in 42 k-4.2508909476655425,b-4.2508909476655425 ,loss132.4913922651612\n",
      "we found in 43 k-4.247616785583968,b-4.247616785583968 ,loss132.4478054548639\n",
      "we found in 44 k-4.244343426328531,b-4.244343426328531 ,loss132.40424001696005\n",
      "we found in 45 k-4.2410708697023765,b-4.2410708697023765 ,loss132.36069594097032\n",
      "we found in 46 k-4.237799115508698,b-4.237799115508698 ,loss132.31717321641966\n",
      "we found in 47 k-4.234528163550741,b-4.234528163550741 ,loss132.27367183283874\n",
      "we found in 48 k-4.231258013631796,b-4.231258013631796 ,loss132.2301917797632\n",
      "we found in 49 k-4.227988665555202,b-4.227988665555202 ,loss132.1867330467338\n",
      "we found in 50 k-4.224720119124348,b-4.224720119124348 ,loss132.14329562329667\n",
      "we found in 51 k-4.22145237414267,b-4.22145237414267 ,loss132.09987949900267\n",
      "we found in 52 k-4.218185430413652,b-4.218185430413652 ,loss132.05648466340776\n",
      "we found in 53 k-4.214919287740826,b-4.214919287740826 ,loss132.0131111060737\n",
      "we found in 54 k-4.2116539459277735,b-4.2116539459277735 ,loss131.96975881656638\n",
      "we found in 55 k-4.2083894047781225,b-4.2083894047781225 ,loss131.92642778445776\n",
      "we found in 56 k-4.205125664095551,b-4.205125664095551 ,loss131.88311799932413\n",
      "we found in 57 k-4.201862723683782,b-4.201862723683782 ,loss131.83982945074746\n",
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      "we found in 1902 k0.6398831104105506,b0.6398831104105506 ,loss79.30057885763114\n",
      "we found in 1903 k0.6419580523127019,b0.6419580523127019 ,loss79.28307367599949\n",
      "we found in 1904 k0.6440324854380889,b0.6440324854380889 ,loss79.26557707787371\n",
      "we found in 1905 k0.6461064099114638,b0.6461064099114638 ,loss79.24808905904462\n",
      "we found in 1906 k0.6481798258575485,b0.6481798258575485 ,loss79.23060961530544\n",
      "we found in 1907 k0.6502527334010342,b0.6502527334010342 ,loss79.21313874245169\n",
      "we found in 1908 k0.6523251326665818,b0.6523251326665818 ,loss79.19567643628052\n",
      "we found in 1909 k0.6543970237788205,b0.6543970237788205 ,loss79.17822269259152\n",
      "we found in 1910 k0.6564684068623503,b0.6564684068623503 ,loss79.16077750718587\n",
      "we found in 1911 k0.6585392820417393,b0.6585392820417393 ,loss79.14334087586741\n",
      "we found in 1912 k0.6606096494415264,b0.6606096494415264 ,loss79.12591279444158\n",
      "we found in 1913 k0.6626795091862201,b0.6626795091862201 ,loss79.10849325871584\n",
      "we found in 1914 k0.6647488614002964,b0.6647488614002964 ,loss79.09108226450023\n",
      "we found in 1915 k0.666817706208203,b0.666817706208203 ,loss79.07367980760618\n",
      "we found in 1916 k0.6688860437343556,b0.6688860437343556 ,loss79.0562858838479\n",
      "we found in 1917 k0.6709538741031398,b0.6709538741031398 ,loss79.03890048904067\n",
      "we found in 1918 k0.6730211974389115,b0.6730211974389115 ,loss79.02152361900286\n",
      "we found in 1919 k0.675088013865995,b0.675088013865995 ,loss79.00415526955423\n",
      "we found in 1920 k0.6771543235086844,b0.6771543235086844 ,loss78.98679543651687\n",
      "we found in 1921 k0.6792201264912439,b0.6792201264912439 ,loss78.96944411571471\n",
      "we found in 1922 k0.6812854229379073,b0.6812854229379073 ,loss78.95210130297416\n",
      "we found in 1923 k0.6833502129728763,b0.6833502129728763 ,loss78.93476699412295\n",
      "we found in 1924 k0.6854144967203237,b0.6854144967203237 ,loss78.91744118499162\n",
      "we found in 1925 k0.6874782743043918,b0.6874782743043918 ,loss78.90012387141209\n",
      "we found in 1926 k0.6895415458491915,b0.6895415458491915 ,loss78.88281504921899\n",
      "we found in 1927 k0.6916043114788045,b0.6916043114788045 ,loss78.86551471424835\n",
      "we found in 1928 k0.6936665713172806,b0.6936665713172806 ,loss78.84822286233863\n",
      "we found in 1929 k0.6957283254886412,b0.6957283254886412 ,loss78.83093948933049\n",
      "we found in 1930 k0.6977895741168748,b0.6977895741168748 ,loss78.813664591066\n",
      "we found in 1931 k0.6998503173259415,b0.6998503173259415 ,loss78.79639816339001\n",
      "we found in 1932 k0.7019105552397709,b0.7019105552397709 ,loss78.77914020214871\n",
      "we found in 1933 k0.7039702879822609,b0.7039702879822609 ,loss78.7618907031907\n",
      "we found in 1934 k0.70602951567728,b0.70602951567728 ,loss78.74464966236675\n",
      "we found in 1935 k0.7080882384486663,b0.7080882384486663 ,loss78.72741707552947\n",
      "we found in 1936 k0.7101464564202267,b0.7101464564202267 ,loss78.7101929385334\n",
      "we found in 1937 k0.7122041697157389,b0.7122041697157389 ,loss78.69297724723528\n",
      "we found in 1938 k0.7142613784589497,b0.7142613784589497 ,loss78.67576999749397\n",
      "we found in 1939 k0.7163180827735749,b0.7163180827735749 ,loss78.65857118517005\n",
      "we found in 1940 k0.7183742827833013,b0.7183742827833013 ,loss78.64138080612642\n",
      "we found in 1941 k0.7204299786117847,b0.7204299786117847 ,loss78.62419885622778\n",
      "we found in 1942 k0.7224851703826505,b0.7224851703826505 ,loss78.60702533134129\n",
      "we found in 1943 k0.7245398582194934,b0.7245398582194934 ,loss78.58986022733536\n",
      "we found in 1944 k0.7265940422458788,b0.7265940422458788 ,loss78.5727035400812\n",
      "we found in 1945 k0.7286477225853414,b0.7286477225853414 ,loss78.55555526545173\n",
      "we found in 1946 k0.7307008993613847,b0.7307008993613847 ,loss78.53841539932155\n",
      "we found in 1947 k0.7327535726974838,b0.7327535726974838 ,loss78.52128393756823\n",
      "we found in 1948 k0.7348057427170814,b0.7348057427170814 ,loss78.50416087607015\n",
      "we found in 1949 k0.7368574095435921,b0.7368574095435921 ,loss78.48704621070883\n",
      "we found in 1950 k0.7389085733003982,b0.7389085733003982 ,loss78.4699399373671\n",
      "we found in 1951 k0.7409592341108524,b0.7409592341108524 ,loss78.45284205192979\n",
      "we found in 1952 k0.7430093920982781,b0.7430093920982781 ,loss78.4357525502843\n",
      "we found in 1953 k0.7450590473859667,b0.7450590473859667 ,loss78.41867142831948\n",
      "we found in 1954 k0.7471082000971818,b0.7471082000971818 ,loss78.40159868192647\n",
      "we found in 1955 k0.7491568503551549,b0.7491568503551549 ,loss78.38453430699863\n",
      "we found in 1956 k0.7512049982830872,b0.7512049982830872 ,loss78.36747829943076\n",
      "we found in 1957 k0.7532526440041513,b0.7532526440041513 ,loss78.35043065512012\n",
      "we found in 1958 k0.7552997876414875,b0.7552997876414875 ,loss78.33339136996607\n",
      "we found in 1959 k0.7573464293182077,b0.7573464293182077 ,loss78.31636043986938\n",
      "we found in 1960 k0.7593925691573928,b0.7593925691573928 ,loss78.29933786073353\n",
      "we found in 1961 k0.7614382072820931,b0.7614382072820931 ,loss78.28232362846362\n",
      "we found in 1962 k0.7634833438153298,b0.7634833438153298 ,loss78.26531773896684\n",
      "we found in 1963 k0.7655279788800934,b0.7655279788800934 ,loss78.2483201881523\n",
      "we found in 1964 k0.7675721125993433,b0.7675721125993433 ,loss78.23133097193126\n",
      "we found in 1965 k0.7696157450960107,b0.7696157450960107 ,loss78.21435008621694\n",
      "we found in 1966 k0.7716588764929951,b0.7716588764929951 ,loss78.19737752692478\n",
      "we found in 1967 k0.7737015069131672,b0.7737015069131672 ,loss78.18041328997151\n",
      "we found in 1968 k0.7757436364793651,b0.7757436364793651 ,loss78.16345737127672\n",
      "we found in 1969 k0.7777852653144004,b0.7777852653144004 ,loss78.14650976676148\n",
      "we found in 1970 k0.7798263935410513,b0.7798263935410513 ,loss78.12957047234904\n",
      "we found in 1971 k0.7818670212820672,b0.7818670212820672 ,loss78.11263948396474\n",
      "we found in 1972 k0.7839071486601679,b0.7839071486601679 ,loss78.09571679753554\n",
      "we found in 1973 k0.7859467757980428,b0.7859467757980428 ,loss78.07880240899082\n",
      "we found in 1974 k0.7879859028183509,b0.7879859028183509 ,loss78.06189631426176\n",
      "we found in 1975 k0.790024529843721,b0.790024529843721 ,loss78.04499850928167\n",
      "we found in 1976 k0.7920626569967526,b0.7920626569967526 ,loss78.02810898998557\n",
      "we found in 1977 k0.7941002844000138,b0.7941002844000138 ,loss78.01122775231067\n",
      "we found in 1978 k0.7961374121760445,b0.7961374121760445 ,loss77.99435479219623\n",
      "we found in 1979 k0.7981740404473525,b0.7981740404473525 ,loss77.9774901055834\n",
      "we found in 1980 k0.8002101693364174,b0.8002101693364174 ,loss77.96063368841531\n",
      "we found in 1981 k0.8022457989656876,b0.8022457989656876 ,loss77.94378553663717\n",
      "we found in 1982 k0.8042809294575817,b0.8042809294575817 ,loss77.92694564619613\n",
      "we found in 1983 k0.806315560934489,b0.806315560934489 ,loss77.91011401304127\n",
      "we found in 1984 k0.8083496935187677,b0.8083496935187677 ,loss77.89329063312364\n",
      "we found in 1985 k0.8103833273327469,b0.8103833273327469 ,loss77.87647550239652\n",
      "we found in 1986 k0.8124164624987253,b0.8124164624987253 ,loss77.85966861681477\n",
      "we found in 1987 k0.8144490991389712,b0.8144490991389712 ,loss77.8428699723357\n",
      "we found in 1988 k0.8164812373757235,b0.8164812373757235 ,loss77.82607956491826\n",
      "we found in 1989 k0.8185128773311912,b0.8185128773311912 ,loss77.8092973905235\n",
      "we found in 1990 k0.8205440191275527,b0.8205440191275527 ,loss77.79252344511426\n",
      "we found in 1991 k0.8225746628869575,b0.8225746628869575 ,loss77.77575772465576\n",
      "we found in 1992 k0.8246048087315241,b0.8246048087315241 ,loss77.75900022511492\n",
      "we found in 1993 k0.8266344567833415,b0.8266344567833415 ,loss77.7422509424606\n",
      "we found in 1994 k0.8286636071644689,b0.8286636071644689 ,loss77.72550987266388\n",
      "we found in 1995 k0.8306922599969351,b0.8306922599969351 ,loss77.70877701169726\n",
      "we found in 1996 k0.8327204154027398,b0.8327204154027398 ,loss77.69205235553618\n",
      "we found in 1997 k0.8347480735038514,b0.8347480735038514 ,loss77.67533590015714\n",
      "we found in 1998 k0.8367752344222099,b0.8367752344222099 ,loss77.65862764153896\n",
      "we found in 1999 k0.8388018982797247,b0.8388018982797247 ,loss77.64192757566262\n"
     ]
    }
   ],
   "source": [
    "def correct_k(k,b,x,y):\n",
    "    return 2*np.mean(x*(k*x+b-y))\n",
    "def correct_b(k,b,x,y):\n",
    "    return 2*np.mean(k*x+b-y)\n",
    "\n",
    "b=random.randrange(-100,100)\n",
    "k=random.randrange(-100,100)\n",
    "best_k=None\n",
    "best_b=None\n",
    "min_loss=float('inf')\n",
    "learn_rate=0.01\n",
    "x = dataframe['RM']\n",
    "y = dataframe['price']\n",
    "for i in range(2000):\n",
    "\n",
    "    k=k-correct_k(k,b,x,y)*learn_rate\n",
    "    b=b-correct_b(k,b,x, y)*learn_rate\n",
    "    y_hat=k*x+b\n",
    "    loss2=loss(y_hat,y)\n",
    "    # print(loss2)\n",
    "    if loss2<min_loss:\n",
    "        min_loss = loss2\n",
    "        best_k, best_b = k, b\n",
    "        print('we found in {} k{},b{} ,loss{}'.format(i,best_k,best_k,min_loss))"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "outputs": [],
   "source": [],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
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   "language": "python",
   "display_name": "PyCharm (neuralnrtwork)"
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  "language_info": {
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   "file_extension": ".py",
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